What we expect to see

Reality, across different methods

Heat maps - Additional TIEs if naive model fit

Binary Outcomes - GLMM

TIElimits <- c(0, .3)
invisible(myfacet_glmm_bin <- facet_grid(cols = vars(baseline_logit), rows = vars(nsub), labeller = label_both))
invisible(myfacet_glmm_pois <- facet_grid(cols = vars(baseline), rows = vars(nsub), labeller = label_both))

sas_bin_nc5 <- add_lower_upper(data.frame(read.csv("glmm_sas_bin_nc5.csv", header = T)))

ggplot(data = sas_bin_nc5 %>% mutate(sb2_diff = log(abs(sb2_1 - sb2_2)))) +
  geom_point(aes(x = TIE_flex, y = TIE_naive, color = sb2_diff)) +
  myhline + myvline + mydiagline +
  labs(x = "Flexible TIE rate", y = "Naive TIE rate", title = "Wald, SAS, BW DF, GLMM binary outcomes") +
  xlim(TIElimits) + ylim(TIElimits) + myscale + myfacet_glmm_bin +
  my_grid_theme

TIElimits <- c(0, .15)
ggplot(data = sas_bin_nc5 %>% mutate(sb2_diff = log(abs(sb2_1 - sb2_2)))) +
  geom_point(aes(x = TIE_flex, y = TIE_naive, color = sb2_diff)) +
  myhline + myvline + mydiagline +
  labs(x = "Flexible TIE rate", y = "Naive TIE rate", title = "Wald, SAS, BW DF, GLMM binary outcomes, excluding outliers") +
  xlim(TIElimits) + ylim(TIElimits) + myscale + myfacet_glmm_bin +
  my_grid_theme

TIElimits <- c(0.04, .121)
mod_p10_flexnaive_lme4 <- readRDS("TEMPmodel_lme4_pois10_TEMP_FLEXNAIVE.rds")
ggplot(data = mod_p10_flexnaive_lme4 %>% mutate(sb2_diff = log(abs(sb2_1 - sb2_2)))) +
  geom_point(aes(x = TIE_flex, y = TIE_naive, color = sb2_diff)) +
  myhline + myvline + mydiagline +
  labs(x = "Flexible TIE rate", y = "Naive TIE rate", title = "Wald, lme4, GLMM Poisson outcomes, 10 clusters per arm") +
  xlim(TIElimits) + ylim(TIElimits) + myscale + myfacet_glmm_pois +
  my_grid_theme

lme4_bin_flex50_MOD <- data.frame(readRDS("TEMPmodel_lme4_flex_bin50_TEMP.rds"))
myscale2 <- scale_fill_gradient(name = "log sbsq arm diff", 
                        low = "grey30", high = "red")

ggplot(data = lme4_bin_flex50_MOD %>% mutate(sb2_diff = round(log(abs(sb2_1 - sb2_2)), digits = 2))) +
  geom_dotplot(aes(x = TIE_flex, fill = sb2_diff, color = factor(sb2_diff), stroke = 0)) +
  labs(x = "Flexible TIE rate", title = "Wald, lme4 Binary outcomes, 50 clusters per arm, FLEXIBLE only") +
  facet_grid(rows = vars(nsub), cols = vars(baseline)) + myscale2 + xlim(c(0, .2))

lme4_bin_flex50_LRT <- data.frame(readRDS("TEMPlrt_lme4_flex_bin50_TEMP.rds"))
ggplot(data = lme4_bin_flex50_LRT %>% mutate(sb2_diff = round(log(abs(sb2_1 - sb2_2)), digits = 2))) +
  geom_dotplot(aes(x = TIE_flex, fill = sb2_diff, color = factor(sb2_diff), stroke = 0)) +
  labs(x = "Flexible TIE rate", title = "LRT, lme4 Binary outcomes, 50 clusters per arm, FLEXIBLE only") +
  facet_grid(rows = vars(nsub), cols = vars(baseline)) + myscale2 + xlim(c(0, .2))